def other_sentence(type, request, sentence):
"""
process the other from of a sentence
Input=type and requesting of sentence and the sentence
Output=class Sentence
"""
#init
vg = VerbalGroup([], [], '', [], [], [], [], VerbalGroup.affirmative, [])
analysis = Sentence(type, request, [], [])
modal = []
if not sentence:
return []
#We have to add punctuation if there is not
if sentence[len(sentence) - 1] not in ['.', '?', '!']:
sentence = sentence + ['.']
#We start with determination of probably second verb in subsentence
sentence = other_functions.find_scd_verb_sub(sentence)
#We search the subject
sbj = analyse_nominal_group.find_sn_pos(sentence, 0)
if sbj != [] or type == RELATIVE:
#If we haven't a data type => it is a statement
if type == '':
analysis.data_type = STATEMENT
#We have to separate the case using these, this or there
if sentence[0] in ResourcePool().demonstrative_det and analyse_verb.infinitive([sentence[1]],
'present simple') == ['be']:
#We recover this information and remove it
analysis.sn = [NominalGroup([sentence[0]], [], [], [], [])]
if sentence[0] == 'there' and sentence[1] == 'are':
analysis.sn[0]._quantifier = 'SOME'
sentence = sentence[1:]
if not analysis.sn:
#We recover the subject
sentence = analyse_nominal_structure.recover_ns(sentence, analysis, 0)
#End of the sentence? -> nominal sentence
if sentence == [] or sentence[0] in ['.', '!', '?']:
analysis.sv = []
return analysis
#We have to know if there is a modal
if sentence[0] in ResourcePool().modal:
modal = sentence[0]
if modal == 'can' or modal == 'must' or modal == 'shall' or modal == 'may':
sentence = sentence[1:]
#We must take into account all possible cases to recover the sentence's tense
if len(sentence) > 1 and sentence[1] == 'not':
vg.state = VerbalGroup.negative
#Before the negative form we have an auxiliary for the negation
if sentence[0] == 'do' or sentence[0] == 'does' or sentence[0] == 'did':
vg.vrb_tense = analyse_verb.find_tense_statement([sentence[0]])
sentence = sentence[2:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
#There is a modal
elif modal:
sentence = [sentence[0]] + sentence[2:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_statement(sentence)
else:
#We remove 'not' and find the tense
sentence = sentence[:1] + sentence[2:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_statement(sentence)
#For the affirmative processing
else:
if sentence[0] == 'not':
vg.state = VerbalGroup.negative
sentence = sentence[1:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_statement(sentence)
verb = analyse_verb.find_verb_statement(sentence, vg.vrb_tense)
verb_main = analyse_verb.return_verb(sentence, verb, vg.vrb_tense)
vg.vrb_main = [other_functions.convert_to_string(verb_main)]
#We delete the verb
sentence = sentence[sentence.index(verb[0]) + len(verb_main):]
#We perform the processing with the modal
if modal:
vg.vrb_main = [modal + '+' + vg.vrb_main[0]]
#This is a imperative form
else:
#re-init
analysis.data_type = IMPERATIVE
vg.vrb_tense = 'present simple'
if sentence[0] in ResourcePool().proposals:
sentence = ['.'] + sentence
#Negative form
if sentence[1] == 'not':
sentence = sentence[sentence.index('not') + 1:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.state = VerbalGroup.negative
else:
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
#We process the verb
verb = [sentence[0]]
verb_main = analyse_verb.return_verb(sentence, verb, vg.vrb_tense)
vg.vrb_main = [other_functions.convert_to_string(verb_main)]
#We delete the verb
sentence = sentence[sentence.index(verb[0]) + len(verb_main):]
if sentence and sentence[-1] == '?':
analysis.data_type = YES_NO_QUESTION
#We recover the conjunctive subsentence
sentence = analyse_verbal_structure.process_conjunctive_sub(sentence, vg)
#It verifies if there is a secondary verb
sec_vrb = analyse_verbal_structure.find_scd_vrb(sentence)
if sec_vrb:
sentence = analyse_verbal_structure.process_scd_sentence(sentence, vg, sec_vrb)
#We recover the subsentence
sentence = analyse_verbal_structure.process_subsentence(sentence, vg)
if sentence != [] and vg.vrb_main != []:
#Process relative changes
sentence = analyse_verbal_structure.correct_i_compl(sentence, vg.vrb_main[0])
sentence = analyse_verbal_structure.process_compare(sentence, vg)
sentence = analyse_nominal_group.find_plural(sentence)
#We recover the direct, indirect complement and the adverbial
sentence = analyse_verbal_structure.recover_obj_iobj(sentence, vg)
#We have to take off abverbs form the sentence
sentence = analyse_verbal_structure.find_adv(sentence, vg)
#In case there is a state verb followed by an adjective
sentence = analyse_verbal_structure.state_adjective(sentence, vg)
#If there is a forgotten
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
while len(sentence) > 1:
stc = analyse_verbal_structure.create_nom_gr(sentence, request)
#We recover the direct, indirect complement and the adverbial
stc = analyse_verbal_structure.recover_obj_iobj(stc, vg)
if stc == sentence:
#We leave the loop
break
else:
sentence = stc
vg = analyse_verbal_structure.refine_indirect_complement(vg)
vg = analyse_verbal_structure.refine_subsentence(vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
analysis.sv = [vg]
return analysis
def other_sentence(type, request, sentence):
"""
process the other from of a sentence
Input=type and requesting of sentence and the sentence
Output=class Sentence
"""
#init
vg = VerbalGroup([], [], '', [], [], [], [], VerbalGroup.affirmative, [])
analysis = Sentence(type, request, [], [])
modal = []
if not sentence:
return []
#We have to add punctuation if there is not
if sentence[len(sentence) - 1] not in ['.', '?', '!']:
sentence = sentence + ['.']
#We start with determination of probably second verb in subsentence
sentence = other_functions.find_scd_verb_sub(sentence)
#We search the subject
sbj = analyse_nominal_group.find_sn_pos(sentence, 0)
if sbj != [] or type == RELATIVE:
#If we haven't a data type => it is a statement
if type == '':
analysis.data_type = STATEMENT
#We have to separate the case using these, this or there
if sentence[0] in ResourcePool(
).demonstrative_det and analyse_verb.infinitive(
[sentence[1]], 'present simple') == ['be']:
#We recover this information and remove it
analysis.sn = [NominalGroup([sentence[0]], [], [], [], [])]
if sentence[0] == 'there' and sentence[1] == 'are':
analysis.sn[0]._quantifier = 'SOME'
sentence = sentence[1:]
if not analysis.sn:
#We recover the subject
sentence = analyse_nominal_structure.recover_ns(
sentence, analysis, 0)
#End of the sentence? -> nominal sentence
if sentence == [] or sentence[0] in ['.', '!', '?']:
analysis.sv = []
return analysis
#We have to know if there is a modal
if sentence[0] in ResourcePool().modal:
modal = sentence[0]
if modal == 'can' or modal == 'must' or modal == 'shall' or modal == 'may':
sentence = sentence[1:]
#We must take into account all possible cases to recover the sentence's tense
if len(sentence) > 1 and sentence[1] == 'not':
vg.state = VerbalGroup.negative
#Before the negative form we have an auxiliary for the negation
if sentence[0] == 'do' or sentence[0] == 'does' or sentence[
0] == 'did':
vg.vrb_tense = analyse_verb.find_tense_statement([sentence[0]])
sentence = sentence[2:]
sentence = analyse_verbal_structure.delete_unusable_word(
sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
#There is a modal
elif modal:
sentence = [sentence[0]] + sentence[2:]
sentence = analyse_verbal_structure.delete_unusable_word(
sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_statement(sentence)
else:
#We remove 'not' and find the tense
sentence = sentence[:1] + sentence[2:]
sentence = analyse_verbal_structure.delete_unusable_word(
sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_statement(sentence)
#For the affirmative processing
else:
if sentence[0] == 'not':
vg.state = VerbalGroup.negative
sentence = sentence[1:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_statement(sentence)
verb = analyse_verb.find_verb_statement(sentence, vg.vrb_tense)
verb_main = analyse_verb.return_verb(sentence, verb, vg.vrb_tense)
vg.vrb_main = [other_functions.convert_to_string(verb_main)]
#We delete the verb
sentence = sentence[sentence.index(verb[0]) + len(verb_main):]
#We perform the processing with the modal
if modal:
vg.vrb_main = [modal + '+' + vg.vrb_main[0]]
#This is a imperative form
else:
#re-init
analysis.data_type = IMPERATIVE
vg.vrb_tense = 'present simple'
if sentence[0] in ResourcePool().proposals:
sentence = ['.'] + sentence
#Negative form
if sentence[1] == 'not':
sentence = sentence[sentence.index('not') + 1:]
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.state = VerbalGroup.negative
else:
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
#We process the verb
verb = [sentence[0]]
verb_main = analyse_verb.return_verb(sentence, verb, vg.vrb_tense)
vg.vrb_main = [other_functions.convert_to_string(verb_main)]
#We delete the verb
sentence = sentence[sentence.index(verb[0]) + len(verb_main):]
if sentence and sentence[-1] == '?':
analysis.data_type = YES_NO_QUESTION
#We recover the conjunctive subsentence
sentence = analyse_verbal_structure.process_conjunctive_sub(sentence, vg)
#It verifies if there is a secondary verb
sec_vrb = analyse_verbal_structure.find_scd_vrb(sentence)
if sec_vrb:
sentence = analyse_verbal_structure.process_scd_sentence(
sentence, vg, sec_vrb)
#We recover the subsentence
sentence = analyse_verbal_structure.process_subsentence(sentence, vg)
if sentence != [] and vg.vrb_main != []:
#Process relative changes
sentence = analyse_verbal_structure.correct_i_compl(
sentence, vg.vrb_main[0])
sentence = analyse_verbal_structure.process_compare(sentence, vg)
sentence = analyse_nominal_group.find_plural(sentence)
#We recover the direct, indirect complement and the adverbial
sentence = analyse_verbal_structure.recover_obj_iobj(sentence, vg)
#We have to take off abverbs form the sentence
sentence = analyse_verbal_structure.find_adv(sentence, vg)
#In case there is a state verb followed by an adjective
sentence = analyse_verbal_structure.state_adjective(sentence, vg)
#If there is a forgotten
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
while len(sentence) > 1:
stc = analyse_verbal_structure.create_nom_gr(sentence, request)
#We recover the direct, indirect complement and the adverbial
stc = analyse_verbal_structure.recover_obj_iobj(stc, vg)
if stc == sentence:
#We leave the loop
break
else:
sentence = stc
vg = analyse_verbal_structure.refine_indirect_complement(vg)
vg = analyse_verbal_structure.refine_subsentence(vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
analysis.sv = [vg]
return analysis
def y_n_ques(type, request, sentence):
"""
process the yes or no question from of a sentence
Input=type and requesting of sentence and the sentence
Output=class Sentence
"""
#init
vg = VerbalGroup([], [], '', [], [], [], [], VerbalGroup.affirmative, [])
analysis = Sentence(type, request, [], [])
modal = []
stc = sentence
#We start with determination of probably second verb in subsentence
sentence = other_functions.find_scd_verb_sub(sentence)
#We have to add punctuation if there is not
if sentence == [] or sentence[0] == '.' or sentence[0] == '?' or sentence[0] == '!':
#We have probably the aim as an adverb
analyse_verbal_structure.find_adv([request], vg)
analysis.aim = 'thing'
analysis.sv = [vg]
return analysis
#We recover the auxiliary
aux = sentence[0]
#We have to know if there is a modal
if aux in ResourcePool().modal:
modal = aux
#If we have a negative form
if sentence[1] == 'not':
vg.state = VerbalGroup.negative
#We remove 'not'
sentence = sentence[:1] + sentence[2:]
#Wrong is a noun but not followed by the determinant
if sentence[1] == 'wrong' and request == 'thing':
analysis.sn = [NominalGroup([], [], ['wrong'], [], [])]
sentence = [sentence[0]] + sentence[2:]
#In this case we have an imperative sentence
elif analyse_nominal_group.find_sn_pos(sentence, 1) == [] and type != W_QUESTION:
#We have to reput the 'not'
if vg.state == VerbalGroup.negative:
sentence = sentence[:1] + ['not'] + sentence[1:]
return other_sentence(type, request, sentence)
#We delete the auxiliary
sentence = sentence[1:]
#We have to separate the case using these, this or there
if sentence[0] in ResourcePool().demonstrative_det and analyse_verb.infinitive([aux], 'present simple') == ['be']:
#If we have a verb or an adverb just after (if not, we have a noun)
if sentence[0].endswith('ed') or sentence[0].endswith('ing') or sentence[0].endswith('ly') or sentence[
0] in ResourcePool().adverbs:
#We recover this information and remove it
analysis.sn = [NominalGroup([sentence[0]], [], [], [], [])]
if sentence[0] == 'there' and aux == 'are':
analysis.sn[0]._quantifier = 'SOME'
sentence = sentence[1:]
if not analysis.sn:
#We recover the subject
sentence = analyse_nominal_structure.recover_ns(sentence, analysis, 0)
if aux == 'do' and not analyse_verbal_structure.can_be_imperative(sentence):
return other_sentence('', '', stc)
#If there is one element => it is an auxiliary => verb 'be'
if len(sentence) == 0:
vg.vrb_tense = analyse_verb.find_tense_statement(aux)
vg.vrb_main = ['be']
else:
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_question(sentence, aux)
#We process the verb
verb = analyse_verb.find_verb_question(sentence, aux, vg.vrb_tense)
verb_main = analyse_verb.return_verb(sentence, verb, vg.vrb_tense)
vg.vrb_main = [other_functions.convert_to_string(verb_main)]
#We delete the verb if the aux is not the verb 'be'
if vg.vrb_main != ['be']:
sentence = sentence[sentence.index(verb[0]) + len(verb_main):]
elif sentence[0] == 'be':
sentence = sentence[1:]
#Here we have special processing for different cases
if sentence:
#For 'what' descrition case
if sentence[0] == 'like' and aux != 'would':
vg.vrb_main = ['like']
sentence = sentence[1:]
#For 'how' questions with often
elif sentence[0].endswith('ing') and not (sentence[0].endswith('thing')):
vg.vrb_main[0] = vg.vrb_main[0] + '+' + sentence[0]
sentence = sentence[1:]
#We recover the conjunctive subsentence
sentence = analyse_verbal_structure.process_conjunctive_sub(sentence, vg)
#It verifies if there is a secondary verb
sec_vrb = analyse_verbal_structure.find_scd_vrb(sentence)
if sec_vrb:
sentence = analyse_verbal_structure.process_scd_sentence(sentence, vg, sec_vrb)
#We recover the subsentence
sentence = analyse_verbal_structure.process_subsentence(sentence, vg)
#Process relative changes
sentence = analyse_verbal_structure.correct_i_compl(sentence, vg.vrb_main[0])
sentence = analyse_verbal_structure.process_compare(sentence, vg)
sentence = analyse_nominal_group.find_plural(sentence)
#We recover the direct, indirect complement and the adverbial
sentence = analyse_verbal_structure.recover_obj_iobj(sentence, vg)
#We have to take off adverbs form the sentence
sentence = analyse_verbal_structure.find_adv(sentence, vg)
#We perform the processing with the modal
if modal:
vg.vrb_main = [modal + '+' + vg.vrb_main[0]]
#If there is a forgotten
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
#In case there is a state verb followed by an adjective
sentence = analyse_verbal_structure.state_adjective(sentence, vg)
#We have to correct the mistake of the subject
for p in ResourcePool().demonstrative_det:
if analysis.sn and analysis.sn[0].det == [p] and analysis.sn[0].noun == []:
if sentence != [0] and sentence[0] == '.' and sentence[0] == '?' and sentence[0] == '!':
if sentence[0] in ResourcePool().proposals:
pass
else:
analysis.sn[0].noun = [sentence[0]]
sentence = sentence[1:]
sentence = analyse_verbal_structure.state_adjective(sentence, vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
while len(sentence) > 1:
stc = analyse_verbal_structure.create_nom_gr(sentence, request)
#We recover the direct, indirect complement and the adverbial
stc = analyse_verbal_structure.recover_obj_iobj(stc, vg)
if stc == sentence:
#We leave the loop
break
else:
sentence = stc
vg = analyse_verbal_structure.refine_indirect_complement(vg)
vg = analyse_verbal_structure.refine_subsentence(vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
analysis.sv = [vg]
return analysis
def y_n_ques(type, request, sentence):
"""
process the yes or no question from of a sentence
Input=type and requesting of sentence and the sentence
Output=class Sentence
"""
#init
vg = VerbalGroup([], [], '', [], [], [], [], VerbalGroup.affirmative, [])
analysis = Sentence(type, request, [], [])
modal = []
stc = sentence
#We start with determination of probably second verb in subsentence
sentence = other_functions.find_scd_verb_sub(sentence)
#We have to add punctuation if there is not
if sentence == [] or sentence[0] == '.' or sentence[0] == '?' or sentence[
0] == '!':
#We have probably the aim as an adverb
analyse_verbal_structure.find_adv([request], vg)
analysis.aim = 'thing'
analysis.sv = [vg]
return analysis
#We recover the auxiliary
aux = sentence[0]
#We have to know if there is a modal
if aux in ResourcePool().modal:
modal = aux
#If we have a negative form
if sentence[1] == 'not':
vg.state = VerbalGroup.negative
#We remove 'not'
sentence = sentence[:1] + sentence[2:]
#Wrong is a noun but not followed by the determinant
if sentence[1] == 'wrong' and request == 'thing':
analysis.sn = [NominalGroup([], [], ['wrong'], [], [])]
sentence = [sentence[0]] + sentence[2:]
#In this case we have an imperative sentence
elif analyse_nominal_group.find_sn_pos(sentence,
1) == [] and type != W_QUESTION:
#We have to reput the 'not'
if vg.state == VerbalGroup.negative:
sentence = sentence[:1] + ['not'] + sentence[1:]
return other_sentence(type, request, sentence)
#We delete the auxiliary
sentence = sentence[1:]
#We have to separate the case using these, this or there
if sentence[0] in ResourcePool(
).demonstrative_det and analyse_verb.infinitive(
[aux], 'present simple') == ['be']:
#If we have a verb or an adverb just after (if not, we have a noun)
if sentence[0].endswith('ed') or sentence[0].endswith(
'ing') or sentence[0].endswith(
'ly') or sentence[0] in ResourcePool().adverbs:
#We recover this information and remove it
analysis.sn = [NominalGroup([sentence[0]], [], [], [], [])]
if sentence[0] == 'there' and aux == 'are':
analysis.sn[0]._quantifier = 'SOME'
sentence = sentence[1:]
if not analysis.sn:
#We recover the subject
sentence = analyse_nominal_structure.recover_ns(sentence, analysis, 0)
if aux == 'do' and not analyse_verbal_structure.can_be_imperative(
sentence):
return other_sentence('', '', stc)
#If there is one element => it is an auxiliary => verb 'be'
if len(sentence) == 0:
vg.vrb_tense = analyse_verb.find_tense_statement(aux)
vg.vrb_main = ['be']
else:
sentence = analyse_verbal_structure.delete_unusable_word(sentence)
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
vg.vrb_tense = analyse_verb.find_tense_question(sentence, aux)
#We process the verb
verb = analyse_verb.find_verb_question(sentence, aux, vg.vrb_tense)
verb_main = analyse_verb.return_verb(sentence, verb, vg.vrb_tense)
vg.vrb_main = [other_functions.convert_to_string(verb_main)]
#We delete the verb if the aux is not the verb 'be'
if vg.vrb_main != ['be']:
sentence = sentence[sentence.index(verb[0]) + len(verb_main):]
elif sentence[0] == 'be':
sentence = sentence[1:]
#Here we have special processing for different cases
if sentence:
#For 'what' descrition case
if sentence[0] == 'like' and aux != 'would':
vg.vrb_main = ['like']
sentence = sentence[1:]
#For 'how' questions with often
elif sentence[0].endswith('ing') and not (
sentence[0].endswith('thing')):
vg.vrb_main[0] = vg.vrb_main[0] + '+' + sentence[0]
sentence = sentence[1:]
#We recover the conjunctive subsentence
sentence = analyse_verbal_structure.process_conjunctive_sub(
sentence, vg)
#It verifies if there is a secondary verb
sec_vrb = analyse_verbal_structure.find_scd_vrb(sentence)
if sec_vrb:
sentence = analyse_verbal_structure.process_scd_sentence(
sentence, vg, sec_vrb)
#We recover the subsentence
sentence = analyse_verbal_structure.process_subsentence(sentence, vg)
#Process relative changes
sentence = analyse_verbal_structure.correct_i_compl(
sentence, vg.vrb_main[0])
sentence = analyse_verbal_structure.process_compare(sentence, vg)
sentence = analyse_nominal_group.find_plural(sentence)
#We recover the direct, indirect complement and the adverbial
sentence = analyse_verbal_structure.recover_obj_iobj(sentence, vg)
#We have to take off adverbs form the sentence
sentence = analyse_verbal_structure.find_adv(sentence, vg)
#We perform the processing with the modal
if modal:
vg.vrb_main = [modal + '+' + vg.vrb_main[0]]
#If there is a forgotten
sentence = analyse_verbal_structure.find_vrb_adv(sentence, vg)
#In case there is a state verb followed by an adjective
sentence = analyse_verbal_structure.state_adjective(sentence, vg)
#We have to correct the mistake of the subject
for p in ResourcePool().demonstrative_det:
if analysis.sn and analysis.sn[0].det == [
p
] and analysis.sn[0].noun == []:
if sentence != [0] and sentence[0] == '.' and sentence[
0] == '?' and sentence[0] == '!':
if sentence[0] in ResourcePool().proposals:
pass
else:
analysis.sn[0].noun = [sentence[0]]
sentence = sentence[1:]
sentence = analyse_verbal_structure.state_adjective(
sentence, vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
while len(sentence) > 1:
stc = analyse_verbal_structure.create_nom_gr(sentence, request)
#We recover the direct, indirect complement and the adverbial
stc = analyse_verbal_structure.recover_obj_iobj(stc, vg)
if stc == sentence:
#We leave the loop
break
else:
sentence = stc
vg = analyse_verbal_structure.refine_indirect_complement(vg)
vg = analyse_verbal_structure.refine_subsentence(vg)
vg = analyse_verbal_structure.DOC_to_IOC(vg)
analysis.sv = [vg]
return analysis